class_docstr: An MOA class, inherited from Optimizer.
  
  This is the designed class to define MOA-related
  variables and methods.
  
  References:
      M.-H. Tayarani and M.-R. Akbarzadeh. Magnetic-inspired optimization algorithms: Operators and structures.
      Swarm and Evolutionary Computation (2014).
class_docstr_tok: ["An","MOA","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","MOA-related","variables","and","methods",".","References",":","M.-H.","Tayarani","and","M.-R.","Akbarzadeh",".","Magnetic-inspired","optimization","algorithms",":","Operators","and","structures",".","Swarm","and","Evolutionary","Computation","(","2014",")","."]
docstr: Initialization method.
  
  Args:
      params: Contains key-value parameters to the meta-heuristics.
docstr_tok: ["Initialization","method",".","Args",":","params",":","Contains","key-value","parameters","to","the","meta-heuristics","."]
code: def __init__(self, params: Optional[Dict[str, Any]] = None) -> None:
      """Initialization method.
  
      Args:
          params: Contains key-value parameters to the meta-heuristics.
  
      """
  
      logger.info("Overriding class: Optimizer -> MOA.")
  
      super(MOA, self).__init__()
  
      self.alpha = 1.0
      self.rho = 2.0
  
      self.build(params)
  
      logger.info("Class overrided.")
  
file_path: opytimizer/opytimizer/optimizers/science/moa.py
imports: from typing import Any, Dict, Optional
  import numpy
  import opytimizer.math.general
  import opytimizer.math.random
  import opytimizer.utils.constant
  import opytimizer.utils.exception
  from opytimizer.core import Optimizer
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 1 to create a new MOA object from inherited base classes: Optimizer with example: obj = MOA(params)